mozilla-foundation/common_voice_13_0
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How to use zuazo/whisper-tiny-es with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="zuazo/whisper-tiny-es") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("zuazo/whisper-tiny-es")
model = AutoModelForSpeechSeq2Seq.from_pretrained("zuazo/whisper-tiny-es")# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("zuazo/whisper-tiny-es")
model = AutoModelForSpeechSeq2Seq.from_pretrained("zuazo/whisper-tiny-es")This model is a fine-tuned version of openai/whisper-tiny on the mozilla-foundation/common_voice_13_0 es dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1801 | 8.0 | 1000 | 0.4318 | 22.1861 |
| 0.1627 | 16.01 | 2000 | 0.4218 | 19.5904 |
| 0.0691 | 24.01 | 3000 | 0.4374 | 20.7170 |
| 0.0124 | 32.01 | 4000 | 0.4635 | 20.0459 |
| 0.0129 | 40.02 | 5000 | 0.4568 | 20.4135 |
If you use these models in your research, please cite:
@misc{dezuazo2025whisperlmimprovingasrmodels,
title={Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages},
author={Xabier de Zuazo and Eva Navas and Ibon Saratxaga and Inma Hernáez Rioja},
year={2025},
eprint={2503.23542},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.23542},
}
Please, check the related paper preprint in arXiv:2503.23542 for more details.
This model is available under the Apache-2.0 License. You are free to use, modify, and distribute this model as long as you credit the original creators.
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="zuazo/whisper-tiny-es")